Evidence map›Paper›PMID 40921087›Full record

ArticleJMIR cancer2025

The Burden of Cancer and Precancerous Conditions Among Transgender Individuals in a Large Health Care Network: Retrospective Cohort Study.

Shuang Yang, Yongqiu Li, Christopher W Wheldon, Jessica Y Islam, Mattia Prosperi, Thomas J George, Elizabeth A Shenkman, Fei Wang, Jiang Bian, Yi Guo

Abstract read
In one paragraph

Article in JMIR cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Shuang YangDepartment of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.ORCID 0000-0002-9890-7668
Yongqiu LiDepartment of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.ORCID 0009-0009-9661-3381
Christopher W WheldonDepartment of Social and Behavioral Sciences, Temple University, Philadelphia, PA, United States.ORCID 0000-0002-0452-0252
Jessica Y IslamDepartment of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, United States.ORCID 0000-0002-3690-3848
Mattia ProsperiDepartment of Epidemiology, Unvirsity of Florida, Gainesville, FL, United States.ORCID 0000-0002-9021-5595
Thomas J GeorgeDepartment of Medicine, University of Florida, Gainesville, FL, United States.ORCID 0000-0002-6249-9180
Elizabeth A ShenkmanDepartment of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.ORCID 0000-0003-4903-1804
Fei WangDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY, United States.ORCID 0000-0001-9459-9461
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.ORCID 0000-0002-2238-5429
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.ORCID 0000-0003-0587-4105

Funding

University of Florida Health Cancer Center Support GrantP30CA247796 · NCI · UNIVERSITY OF FLORIDA · PI Alison Ivey · 2023 to 2026
$10.9M
Disparities of Alzheimer's disease progression in Sexual Minority IndividualsR01AG080624 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Yi Guo · 2023 to 2026
$3.1M
The benefits and harms of lung cancer screening in FloridaR01CA246418 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2023
$1.7M
Using Electronic Health Records from a Large Clinical Data Research Network to Understand Cancer Burden and Cancer Risks Among Transgender and Gender Nonconforming (TGNC) IndividualsR21CA245858 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2021
$773k
Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical TrialsR21AG068717 · NIA · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2021
$419k
Optimizing the Population Representativeness of Older Adults in Cancer TrialsR21CA253394 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2021 to 2021
$392k
NCI NIH HHS P30 CA247796NCI NIH HHS R01 CA246418NCI NIH HHS R21 CA245858NCI NIH HHS R21 CA253394NIA NIH HHS R01 AG080624NIA NIH HHS R21 AG068717
6 · The paper itself

Abstract

Background: Disparities in cancer burden between transgender and cisgender individuals remain an underexplored area of research. Objective: This study aimed to examine the cumulative incidence and associated risk factors for cancer and precancerous conditions among transgender individuals compared with matched cisgender individuals. Methods: We conducted a retrospective cohort study using patient-level electronic health record (EHR) data from the University of Florida Health Integrated Data Repository between 2012 and 2023. Transgender individuals were identified using a validated, computable phenotype algorithm that used structured data and clinical notes. They matched 1:10:10 by age and calendar year of index date with cisgender women and cisgender men. The index date was the first transgender-related record for transgender individuals and a matched diagnosis date for cisgender controls. Primary outcomes included new-onset cancers associated with human papillomavirus, human immunodeficiency virus, tobacco, alcohol, lung, breast, and colorectal sites. Secondary outcomes were precancerous conditions related to the same cancer types. We calculated cumulative incidence rates and conducted time-to-event analyses using the Fine-Gray method, treating all-cause death as a competing risk, to assess associations between gender identity and the presence of cancer or precancer, adjusting for demographic and clinical covariates. Interaction analyses evaluated if associations between cancer risk factors and precancer differed by gender identity. Results: We identified 2745 transgender individuals (mean age at index date 25.1, SD 14.0 years) and matched them with 27,450 cisgender women and 27,450 cisgender men from the same health care system. The cumulative incidence of cancer did not differ significantly between transgender and cisgender cohorts (transgender n=28, 1.0% vs cisgender women, n=358, 1.3%; P=.13 and cisgender men, n=314, 1.1%; P=.64). However, transgender individuals exhibited significantly higher risks for precancerous conditions compared to cisgender women (subdistribution hazard ratios [sHRs] 1.1, 95% CI 1.0-1.3) and cisgender men (sHR 1.3; 95% CI 1.2-1.5). Specifically, transgender individuals were more likely to develop colorectal precancer (sHR 1.2; 95% CI 1.1-1.4) compared to cisgender women, as well as human papillomavirus-related precancer (sHR 1.8; 95% CI 1.4-2.3) and colorectal precancer (sHR 1.4; 95% CI 1.2-1.6) compared to cisgender men. Subgroup analyses showed similar patterns in both female-to-male and male-to-female individuals compared with their matched cisgender counterparts. Interaction analyses revealed stronger protective effects of private insurance or Medicare against precancers in transgender individuals than in cisgender peers, while being non-Hispanic Black or having substantial comorbidities were stronger risk factors among transgender individuals. Conclusions: Transgender individuals showed a similar cancer incidence yet significantly higher precancer incidence compared with cisgender individuals, suggesting underdiagnosis or delayed detection. These findings highlight the need for tailored preventive care strategies, including targeted screenings and risk reduction interventions, to address cancer disparities in the transgender population.

Indexed as

NeoplasmsPrecancerous ConditionsTransgender PersonsAdultFemaleFloridaHumansIncidenceMaleMiddle AgedRetrospective StudiesRisk Factorscancer cumulative incidenceelectronic health recordshealth disparitiesreal-world datatransgender and gender nonconforming

Identifiers

PMID40921087
PMCPMC12416876

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.